全脑磁共振成像代谢产物图像的多项式拟合改进贝叶斯重建方法

Yufang Bao, A. Maudsley
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引用次数: 0

摘要

本文提出了一种多项式拟合改进的贝叶斯方法,用于长回波时间(TE)全脑质子磁共振光谱成像(MRSI)数据的体积代谢物图像重建。该算法采用了一种改进的EM(期望最大化)算法,该算法考虑了厚层磁共振成像中包含的部分体积效应。它结合了高分辨率体积磁共振成像(MRI)作为先验信息。在重建高分辨率代谢物图像之前,进一步结合多项式拟合方法对人工边缘进行平滑处理。我们提出的重建方法成功地将我们现有的二维(2D)代谢物图像重建扩展到3D病例。实验结果表明,重建了分辨率增强的体积代谢物图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Polynomial Fitting Improved Bayesian Reconstruction Method for Whole Brain Volumetric MRSI Metabolite Images
In this paper, a polynomial fitting improved Bayesian approach is proposed for the reconstruction of volumetric metabolite images from long echo time (TE) whole brain proton magnetic resonance spectroscopic imaging (MRSI) data. The proposed algorithm uses a modified EM (expectation maximization) algorithm that takes into account the partial vol- ume effects contained inside a thick slice MRSI. It incorporates high resolution volumetric magnetic resonance imaging (MRI) as a priori information. It further integrates the polynomial fitting method to smooth out artificial edges before the high resolution metabolite images are reconstructed. Our proposed reconstruction method has successfully extended our existing reconstruction of two dimensional (2D) metabolite images to 3D cases. The experimental results show that reso- lution enhanced volumetric metabolite images are reconstructed.
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